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Note publique d'information : This book offers a comprehensive guide to large sample techniques in statistics. With
a focus on developing analytical skills and understanding motivation, Large Sample
Techniques for Statistics begins with fundamental techniques, and connects theory
and applications in engaging ways. The first five chapters review some of the basic
techniques, such as the fundamental epsilon-delta arguments, Taylor expansion, different
types of convergence, and inequalities. The next five chapters discuss limit theorems
in specific situations of observational data. Each of the first ten chapters contains
at least one section of case study. The last six chapters are devoted to special areas
of applications. This new edition introduces a final chapter dedicated to random matrix
theory, as well as expanded treatment of inequalities and mixed effects models. The
book's case studies and applications-oriented chapters demonstrate how to use methods
developed from large sample theory in real world situations. The book is supplemented
by a large number of exercises, giving readers opportunity to practice what they have
learned. Appendices provide context for matrix algebra and mathematical statistics.
The Second Edition seeks to address new challenges in data science. This text is intended
for a wide audience, ranging from senior undergraduate students to researchers with
doctorates. A first course in mathematical statistics and a course in calculus are
prerequisites.
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